Decision making of a vertebrate in response to the sensory signals from the environment is regulated by the neuromodulatory systems in its brain. A vertebrate's behaviors like focusing attention, cautious risk-aversion and curiosity seeking exploration are influenced by these neuromodulatory. The paper presents an autonomous robotic control approach based on vertebrate neuromodulation and its implementation on multiple open-source hardware platforms. A simple neural network is used to model the neuromodulatory functions for generating context based behavioral responses to sensory signals. The neural network incorporates three types of neurons- attention focusing cholinergic and noradrenergic (ACh/NE), curiosity-seeking dopaminergic (DA), and risk aversive serotonergic (5-HT) neurons. The implementation of the neuronal model on multiple relatively simple autonomous robots is illustrated through the interesting behavior of the robots adapting to changes in the environment. The implementation is done in open-source, open-access robotics software framework of Robot Operating System (ROS).

The paper presents a control approach based on vertebrate neuromodulation and its implementation on autonomous robots in the open-source, open-access environment of robot operating system (ROS) within a cloud computing framework. A spiking neural network (SNN) is used to model the neuromodulatory function for generating context based behavioral responses of the robots to sensory input signals. The neural network incorporates three types of neurons- cholinergic and noradrenergic (ACh/NE) neurons for attention focusing and action selection, dopaminergic (DA) neurons for rewards- and curiosity-seeking, and serotonergic (5-HT) neurons for risk aversion behaviors. The model depicts description of neuron activity that is biologically realistic but computationally efficient to allow for large-scale simulation of thousands of neurons. The model is implemented using graphics processing units (GPUs) for parallel computing in real-time using the ROS environment. The model is implemented to study the risk-taking, risk-aversive, and distracted behaviors of the neuromodulated robots in single- and multi-robot configurations. The entire process is implemented in a distributed computing framework using ROS where the robots communicate wirelessly with the computing nodes through the on-board laptops. Results are presented for both single- and multi-robot configurations demonstrating interesting behaviors.